
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


lowercase    :   Dict						=			[
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        'text-classification',
        'language-modeling',
        'summarization',
        'token-classification',
        'question-answering',
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
		import run_clm_flax
		import run_flax_glue
		import run_flax_ner
		import run_mlm_flax
		import run_qa
		import run_summarization_flax
		import run_ta_mlm_flax


logging.basicConfig(level=logging.DEBUG)

lowercase    :   Any						=			logging.getLogger()




def       _SCREAMING_SNAKE_CASE   (						)   ->       Union[str, Any]:






							'''simple docstring'''





							__UpperCamelCase      :  Any       =   argparse.ArgumentParser()
							parser.add_argument("-f")
							__UpperCamelCase      :  str       =   parser.parse_args()
							return args.f




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     str		,    _lowerCamelCase					:     Dict="eval")   ->       List[Any]:






							'''simple docstring'''





							__UpperCamelCase      :  str       =   os.path.join(_lowerCamelCase		,    F'{split}_results.json')
							if os.path.exists(_lowerCamelCase):
														with open(_lowerCamelCase		,    "r") as f:
																					return json.load(_lowerCamelCase)
							raise ValueError(F'can\'t find {path}')


lowercase    :   int						=			logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)




class 					lowerCamelCase__				(							lowercase__):




	'''simple docstring'''




	def   _lowerCamelCase							(   self						:int		)						->					List[str]:
								__UpperCamelCase      :  Tuple       =   self.get_auto_remove_tmp_dir()
								__UpperCamelCase      :  Optional[Any]       =   f'\n            run_glue.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --eval_steps=2\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            '.split()

								with patch.object(SCREAMING_SNAKE_CASE_	,  "argv"	,  SCREAMING_SNAKE_CASE_		):
															run_flax_glue.main()
															__UpperCamelCase      :  Tuple       =   get_results(SCREAMING_SNAKE_CASE_		)
															self.assertGreaterEqual(result["eval_accuracy"]	,  0.75		)







	@slow
	def   _lowerCamelCase							(   self						:int		)						->					int:
								__UpperCamelCase      :  Union[str, Any]       =   self.get_auto_remove_tmp_dir()
								__UpperCamelCase      :  Optional[Any]       =   f'\n            run_clm_flax.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --block_size 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --num_train_epochs 2\n            --logging_steps 2 --eval_steps 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            '.split()

								with patch.object(SCREAMING_SNAKE_CASE_	,  "argv"	,  SCREAMING_SNAKE_CASE_		):
															run_clm_flax.main()
															__UpperCamelCase      :  Tuple       =   get_results(SCREAMING_SNAKE_CASE_		)
															self.assertLess(result["eval_perplexity"]	,  1_0_0		)







	@slow
	def   _lowerCamelCase							(   self						:Optional[int]		)						->					Tuple:
								__UpperCamelCase      :  Any       =   self.get_auto_remove_tmp_dir()
								__UpperCamelCase      :  int       =   f'\n            run_summarization.py\n            --model_name_or_path t5-small\n            --train_file tests/fixtures/tests_samples/xsum/sample.json\n            --validation_file tests/fixtures/tests_samples/xsum/sample.json\n            --test_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --num_train_epochs=3\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --do_predict\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --predict_with_generate\n        '.split()

								with patch.object(SCREAMING_SNAKE_CASE_	,  "argv"	,  SCREAMING_SNAKE_CASE_		):
															run_summarization_flax.main()
															__UpperCamelCase      :  Dict       =   get_results(SCREAMING_SNAKE_CASE_	,  split="test"		)
															self.assertGreaterEqual(result["test_rouge1"]	,  1_0		)
															self.assertGreaterEqual(result["test_rouge2"]	,  2		)
															self.assertGreaterEqual(result["test_rougeL"]	,  7		)
															self.assertGreaterEqual(result["test_rougeLsum"]	,  7		)







	@slow
	def   _lowerCamelCase							(   self						:List[str]		)						->					Dict:
								__UpperCamelCase      :  Union[str, Any]       =   self.get_auto_remove_tmp_dir()
								__UpperCamelCase      :  Dict       =   f'\n            run_mlm.py\n            --model_name_or_path distilroberta-base\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_seq_length 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --logging_steps 2 --eval_steps 2\n            --do_train\n            --do_eval\n            --num_train_epochs=1\n        '.split()

								with patch.object(SCREAMING_SNAKE_CASE_	,  "argv"	,  SCREAMING_SNAKE_CASE_		):
															run_mlm_flax.main()
															__UpperCamelCase      :  str       =   get_results(SCREAMING_SNAKE_CASE_		)
															self.assertLess(result["eval_perplexity"]	,  4_2		)







	@slow
	def   _lowerCamelCase							(   self						:Union[str, Any]		)						->					int:
								__UpperCamelCase      :  Dict       =   self.get_auto_remove_tmp_dir()
								__UpperCamelCase      :  int       =   f'\n            run_t5_mlm_flax.py\n            --model_name_or_path t5-small\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --max_seq_length 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --num_train_epochs 2\n            --logging_steps 2 --eval_steps 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            '.split()

								with patch.object(SCREAMING_SNAKE_CASE_	,  "argv"	,  SCREAMING_SNAKE_CASE_		):
															run_ta_mlm_flax.main()
															__UpperCamelCase      :  Tuple       =   get_results(SCREAMING_SNAKE_CASE_		)
															self.assertGreaterEqual(result["eval_accuracy"]	,  0.42		)







	@slow
	def   _lowerCamelCase							(   self						:Union[str, Any]		)						->					Optional[int]:
								__UpperCamelCase      :  Dict       =   7 if get_gpu_count() > 1 else 2

								__UpperCamelCase      :  Tuple       =   self.get_auto_remove_tmp_dir()
								__UpperCamelCase      :  List[Any]       =   f'\n            run_flax_ner.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/conll/sample.json\n            --validation_file tests/fixtures/tests_samples/conll/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --do_train\n            --do_eval\n            --warmup_steps=2\n            --learning_rate=2e-4\n            --logging_steps 2 --eval_steps 2\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=2\n            --num_train_epochs={epochs}\n            --seed 7\n        '.split()

								with patch.object(SCREAMING_SNAKE_CASE_	,  "argv"	,  SCREAMING_SNAKE_CASE_		):
															run_flax_ner.main()
															__UpperCamelCase      :  Tuple       =   get_results(SCREAMING_SNAKE_CASE_		)
															self.assertGreaterEqual(result["eval_accuracy"]	,  0.75		)
															self.assertGreaterEqual(result["eval_f1"]	,  0.3		)







	@slow
	def   _lowerCamelCase							(   self						:str		)						->					List[str]:
								__UpperCamelCase      :  Optional[Any]       =   self.get_auto_remove_tmp_dir()
								__UpperCamelCase      :  List[Any]       =   f'\n            run_qa.py\n            --model_name_or_path bert-base-uncased\n            --version_2_with_negative\n            --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --num_train_epochs=3\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --logging_steps 2 --eval_steps 2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        '.split()

								with patch.object(SCREAMING_SNAKE_CASE_	,  "argv"	,  SCREAMING_SNAKE_CASE_		):
															run_qa.main()
															__UpperCamelCase      :  str       =   get_results(SCREAMING_SNAKE_CASE_		)
															self.assertGreaterEqual(result["eval_f1"]	,  3_0		)
															self.assertGreaterEqual(result["eval_exact"]	,  3_0		)

"""simple docstring"""






import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
					import torch



@require_torch
@require_optimum
@slow
class 			lowercase					( unittest.TestCase		):






			def 					UpperCAmelCase							(self  :	Tuple			)       ->	Optional[int]:





				"""simple docstring"""


				lowerCAmelCase												=  '''hf-internal-testing/tiny-random-t5'''
				lowerCAmelCase												=  AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  tokenizer('''This is me'''      ,return_tensors='''pt'''			)

				lowerCAmelCase												=  model.to_bettertransformer()

				self.assertTrue(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

				lowerCAmelCase												=  model.generate(**SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  model.reverse_bettertransformer()

				self.assertFalse(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

				with tempfile.TemporaryDirectory() as tmpdirname:
					model.save_pretrained(SCREAMING_SNAKE_CASE_			)

					lowerCAmelCase												=  AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE_			)

					self.assertFalse(
					    any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()			)			)

					lowerCAmelCase												=  model_reloaded.generate(**SCREAMING_SNAKE_CASE_			)
					self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)			)






			def 					UpperCAmelCase							(self  :	str			)       ->	Tuple:





				"""simple docstring"""


				lowerCAmelCase												=  '''hf-internal-testing/tiny-random-t5'''
				lowerCAmelCase												=  AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  model.to_bettertransformer()

				with tempfile.TemporaryDirectory() as tmpdirname:
					with self.assertRaises(SCREAMING_SNAKE_CASE_			):
						model.save_pretrained(SCREAMING_SNAKE_CASE_			)

					lowerCAmelCase												=  model.reverse_bettertransformer()
					model.save_pretrained(SCREAMING_SNAKE_CASE_			)


